Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations119190
Missing cells129208
Missing cells (%)3.4%
Duplicate rows8154
Duplicate rows (%)6.8%
Total size in memory29.1 MiB
Average record size in memory256.0 B

Variable types

Categorical16
Numeric14
Text1
DateTime1

Alerts

Dataset has 8154 (6.8%) duplicate rowsDuplicates
agent is highly overall correlated with hotelHigh correlation
arrival_date_month is highly overall correlated with arrival_date_week_numberHigh correlation
arrival_date_week_number is highly overall correlated with arrival_date_monthHigh correlation
assigned_room_type is highly overall correlated with reserved_room_typeHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
hotel is highly overall correlated with agentHigh correlation
is_canceled is highly overall correlated with reservation_statusHigh correlation
market_segment is highly overall correlated with distribution_channelHigh correlation
reservation_status is highly overall correlated with is_canceledHigh correlation
reserved_room_type is highly overall correlated with assigned_room_typeHigh correlation
children is highly imbalanced (80.7%) Imbalance
babies is highly imbalanced (97.2%) Imbalance
meal is highly imbalanced (53.5%) Imbalance
distribution_channel is highly imbalanced (63.2%) Imbalance
is_repeated_guest is highly imbalanced (79.6%) Imbalance
reserved_room_type is highly imbalanced (58.3%) Imbalance
assigned_room_type is highly imbalanced (51.4%) Imbalance
deposit_type is highly imbalanced (65.3%) Imbalance
customer_type is highly imbalanced (50.6%) Imbalance
required_car_parking_spaces is highly imbalanced (85.4%) Imbalance
agent has 16315 (13.7%) missing values Missing
company has 112402 (94.3%) missing values Missing
previous_cancellations is highly skewed (γ1 = 24.44031598) Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 23.5438383) Skewed
lead_time has 6338 (5.3%) zeros Zeros
stays_in_weekend_nights has 51904 (43.5%) zeros Zeros
stays_in_week_nights has 7635 (6.4%) zeros Zeros
previous_cancellations has 112713 (94.6%) zeros Zeros
previous_bookings_not_canceled has 115573 (97.0%) zeros Zeros
booking_changes has 101147 (84.9%) zeros Zeros
days_in_waiting_list has 115501 (96.9%) zeros Zeros
adr has 1953 (1.6%) zeros Zeros
total_of_special_requests has 70203 (58.9%) zeros Zeros

Reproduction

Analysis started2025-01-03 21:51:08.838921
Analysis finished2025-01-03 21:52:27.910629
Duration1 minute and 19.07 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

hotel
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
City Hotel
79200 
Resort Hotel
39990 

Length

Max length12
Median length10
Mean length10.671029
Min length10

Characters and Unicode

Total characters1271880
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCity Hotel
2nd rowCity Hotel
3rd rowCity Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
City Hotel 79200
66.4%
Resort Hotel 39990
33.6%

Length

2025-01-03T18:52:28.494677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:28.907877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
hotel 119190
50.0%
city 79200
33.2%
resort 39990
 
16.8%

Most occurring characters

ValueCountFrequency (%)
t 238380
18.7%
o 159180
12.5%
e 159180
12.5%
119190
9.4%
H 119190
9.4%
l 119190
9.4%
C 79200
 
6.2%
i 79200
 
6.2%
y 79200
 
6.2%
R 39990
 
3.1%
Other values (2) 79980
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1271880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 238380
18.7%
o 159180
12.5%
e 159180
12.5%
119190
9.4%
H 119190
9.4%
l 119190
9.4%
C 79200
 
6.2%
i 79200
 
6.2%
y 79200
 
6.2%
R 39990
 
3.1%
Other values (2) 79980
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1271880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 238380
18.7%
o 159180
12.5%
e 159180
12.5%
119190
9.4%
H 119190
9.4%
l 119190
9.4%
C 79200
 
6.2%
i 79200
 
6.2%
y 79200
 
6.2%
R 39990
 
3.1%
Other values (2) 79980
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1271880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 238380
18.7%
o 159180
12.5%
e 159180
12.5%
119190
9.4%
H 119190
9.4%
l 119190
9.4%
C 79200
 
6.2%
i 79200
 
6.2%
y 79200
 
6.2%
R 39990
 
3.1%
Other values (2) 79980
 
6.3%

is_canceled
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
0
75039 
1
44151 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119190
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 75039
63.0%
1 44151
37.0%

Length

2025-01-03T18:52:29.263331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:29.542094image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 75039
63.0%
1 44151
37.0%

Most occurring characters

ValueCountFrequency (%)
0 75039
63.0%
1 44151
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75039
63.0%
1 44151
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75039
63.0%
1 44151
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75039
63.0%
1 44151
37.0%

lead_time
Real number (ℝ)

Zeros 

Distinct479
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.01917
Minimum0
Maximum737
Zeros6338
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:29.805293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median69
Q3160
95-th percentile320
Maximum737
Range737
Interquartile range (IQR)142

Descriptive statistics

Standard deviation106.87159
Coefficient of variation (CV)1.027422
Kurtosis1.6969412
Mean104.01917
Median Absolute Deviation (MAD)60
Skewness1.3465801
Sum12398045
Variance11421.536
MonotonicityNot monotonic
2025-01-03T18:52:30.092906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6338
 
5.3%
1 3456
 
2.9%
2 2065
 
1.7%
3 1814
 
1.5%
4 1712
 
1.4%
5 1563
 
1.3%
6 1439
 
1.2%
7 1330
 
1.1%
8 1137
 
1.0%
12 1079
 
0.9%
Other values (469) 97257
81.6%
ValueCountFrequency (%)
0 6338
5.3%
1 3456
2.9%
2 2065
 
1.7%
3 1814
 
1.5%
4 1712
 
1.4%
5 1563
 
1.3%
6 1439
 
1.2%
7 1330
 
1.1%
8 1137
 
1.0%
9 989
 
0.8%
ValueCountFrequency (%)
737 1
 
< 0.1%
709 1
 
< 0.1%
629 17
< 0.1%
626 30
< 0.1%
622 17
< 0.1%
615 17
< 0.1%
608 17
< 0.1%
605 30
< 0.1%
601 17
< 0.1%
594 17
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
2016
56609 
2017
40612 
2015
21969 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters476760
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2016
3rd row2016
4th row2016
5th row2015

Common Values

ValueCountFrequency (%)
2016 56609
47.5%
2017 40612
34.1%
2015 21969
 
18.4%

Length

2025-01-03T18:52:30.550020image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:30.860029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2016 56609
47.5%
2017 40612
34.1%
2015 21969
 
18.4%

Most occurring characters

ValueCountFrequency (%)
2 119190
25.0%
0 119190
25.0%
1 119190
25.0%
6 56609
11.9%
7 40612
 
8.5%
5 21969
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 476760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 119190
25.0%
0 119190
25.0%
1 119190
25.0%
6 56609
11.9%
7 40612
 
8.5%
5 21969
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 476760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 119190
25.0%
0 119190
25.0%
1 119190
25.0%
6 56609
11.9%
7 40612
 
8.5%
5 21969
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 476760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 119190
25.0%
0 119190
25.0%
1 119190
25.0%
6 56609
11.9%
7 40612
 
8.5%
5 21969
 
4.6%

arrival_date_month
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
August
13856 
July
12642 
May
11764 
October
11144 
April
11070 
Other values (7)
58714 

Length

Max length9
Median length7
Mean length5.9034818
Min length3

Characters and Unicode

Total characters703636
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeptember
2nd rowSeptember
3rd rowMarch
4th rowApril
5th rowAugust

Common Values

ValueCountFrequency (%)
August 13856
11.6%
July 12642
10.6%
May 11764
9.9%
October 11144
9.3%
April 11070
9.3%
June 10919
9.2%
September 10495
8.8%
March 9775
8.2%
February 8056
6.8%
November 6775
5.7%
Other values (2) 12694
10.7%

Length

2025-01-03T18:52:31.209249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 13856
11.6%
july 12642
10.6%
may 11764
9.9%
october 11144
9.3%
april 11070
9.3%
june 10919
9.2%
september 10495
8.8%
march 9775
8.2%
february 8056
6.8%
november 6775
5.7%
Other values (2) 12694
10.7%

Most occurring characters

ValueCountFrequency (%)
e 95464
13.6%
r 78065
 
11.1%
u 65253
 
9.3%
b 43240
 
6.1%
a 41443
 
5.9%
y 38386
 
5.5%
t 35495
 
5.0%
J 29485
 
4.2%
c 27689
 
3.9%
A 24926
 
3.5%
Other values (16) 224190
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 703636
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 95464
13.6%
r 78065
 
11.1%
u 65253
 
9.3%
b 43240
 
6.1%
a 41443
 
5.9%
y 38386
 
5.5%
t 35495
 
5.0%
J 29485
 
4.2%
c 27689
 
3.9%
A 24926
 
3.5%
Other values (16) 224190
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 703636
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 95464
13.6%
r 78065
 
11.1%
u 65253
 
9.3%
b 43240
 
6.1%
a 41443
 
5.9%
y 38386
 
5.5%
t 35495
 
5.0%
J 29485
 
4.2%
c 27689
 
3.9%
A 24926
 
3.5%
Other values (16) 224190
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 703636
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 95464
13.6%
r 78065
 
11.1%
u 65253
 
9.3%
b 43240
 
6.1%
a 41443
 
5.9%
y 38386
 
5.5%
t 35495
 
5.0%
J 29485
 
4.2%
c 27689
 
3.9%
A 24926
 
3.5%
Other values (16) 224190
31.9%

arrival_date_week_number
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.165031
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:31.474524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median28
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.605704
Coefficient of variation (CV)0.50085363
Kurtosis-0.98601456
Mean27.165031
Median Absolute Deviation (MAD)11
Skewness-0.010236308
Sum3237800
Variance185.11519
MonotonicityNot monotonic
2025-01-03T18:52:31.991805image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 3574
 
3.0%
30 3082
 
2.6%
32 3038
 
2.5%
34 3035
 
2.5%
18 2920
 
2.4%
28 2850
 
2.4%
21 2847
 
2.4%
17 2799
 
2.3%
20 2776
 
2.3%
29 2759
 
2.3%
Other values (43) 89510
75.1%
ValueCountFrequency (%)
1 1047
0.9%
2 1218
1.0%
3 1317
1.1%
4 1484
1.2%
5 1387
1.2%
6 1505
1.3%
7 2104
1.8%
8 2213
1.9%
9 2115
1.8%
10 2143
1.8%
ValueCountFrequency (%)
53 1815
1.5%
52 1192
1.0%
51 932
0.8%
50 1500
1.3%
49 1782
1.5%
48 1499
1.3%
47 1680
1.4%
46 1570
1.3%
45 1936
1.6%
44 2272
1.9%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.799723
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:32.248114image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.781984
Coefficient of variation (CV)0.55583151
Kurtosis-1.1874011
Mean15.799723
Median Absolute Deviation (MAD)8
Skewness-0.0022192577
Sum1883169
Variance77.123243
MonotonicityNot monotonic
2025-01-03T18:52:32.524350image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17 4401
 
3.7%
5 4310
 
3.6%
15 4188
 
3.5%
25 4155
 
3.5%
26 4143
 
3.5%
9 4089
 
3.4%
12 4084
 
3.4%
16 4071
 
3.4%
2 4051
 
3.4%
19 4041
 
3.4%
Other values (21) 77657
65.2%
ValueCountFrequency (%)
1 3620
3.0%
2 4051
3.4%
3 3852
3.2%
4 3754
3.1%
5 4310
3.6%
6 3825
3.2%
7 3656
3.1%
8 3915
3.3%
9 4089
3.4%
10 3565
3.0%
ValueCountFrequency (%)
31 2205
1.8%
30 3853
3.2%
29 3574
3.0%
28 3943
3.3%
27 3794
3.2%
26 4143
3.5%
25 4155
3.5%
24 3985
3.3%
23 3611
3.0%
22 3591
3.0%

stays_in_weekend_nights
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92772045
Minimum0
Maximum19
Zeros51904
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:32.818140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9986286
Coefficient of variation (CV)1.0764327
Kurtosis7.1807193
Mean0.92772045
Median Absolute Deviation (MAD)1
Skewness1.3801488
Sum110575
Variance0.99725907
MonotonicityNot monotonic
2025-01-03T18:52:33.079664image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 51904
43.5%
2 33259
27.9%
1 30574
25.7%
4 1852
 
1.6%
3 1258
 
1.1%
6 152
 
0.1%
5 79
 
0.1%
8 60
 
0.1%
7 19
 
< 0.1%
9 11
 
< 0.1%
Other values (7) 22
 
< 0.1%
ValueCountFrequency (%)
0 51904
43.5%
1 30574
25.7%
2 33259
27.9%
3 1258
 
1.1%
4 1852
 
1.6%
5 79
 
0.1%
6 152
 
0.1%
7 19
 
< 0.1%
8 60
 
0.1%
9 11
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
16 3
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 5
 
< 0.1%
10 7
 
< 0.1%
9 11
 
< 0.1%
8 60
0.1%
7 19
 
< 0.1%

stays_in_week_nights
Real number (ℝ)

Zeros 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5004363
Minimum0
Maximum50
Zeros7635
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:33.364626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9082907
Coefficient of variation (CV)0.76318308
Kurtosis24.307258
Mean2.5004363
Median Absolute Deviation (MAD)1
Skewness2.8628027
Sum298027
Variance3.6415733
MonotonicityNot monotonic
2025-01-03T18:52:33.668181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 33618
28.2%
1 30255
25.4%
3 22222
18.6%
5 11062
 
9.3%
4 9554
 
8.0%
0 7635
 
6.4%
6 1498
 
1.3%
10 1035
 
0.9%
7 1026
 
0.9%
8 655
 
0.5%
Other values (25) 630
 
0.5%
ValueCountFrequency (%)
0 7635
 
6.4%
1 30255
25.4%
2 33618
28.2%
3 22222
18.6%
4 9554
 
8.0%
5 11062
 
9.3%
6 1498
 
1.3%
7 1026
 
0.9%
8 655
 
0.5%
9 229
 
0.2%
ValueCountFrequency (%)
50 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 5
< 0.1%
26 1
 
< 0.1%

adults
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.856498
Minimum0
Maximum55
Zeros401
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:33.902852image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.57939475
Coefficient of variation (CV)0.31209015
Kurtosis1353.1199
Mean1.856498
Median Absolute Deviation (MAD)0
Skewness18.336632
Sum221276
Variance0.33569828
MonotonicityNot monotonic
2025-01-03T18:52:34.113531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 89530
75.1%
1 22985
 
19.3%
3 6196
 
5.2%
0 401
 
0.3%
4 62
 
0.1%
26 5
 
< 0.1%
27 2
 
< 0.1%
20 2
 
< 0.1%
5 2
 
< 0.1%
40 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 401
 
0.3%
1 22985
 
19.3%
2 89530
75.1%
3 6196
 
5.2%
4 62
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
20 2
 
< 0.1%
26 5
 
< 0.1%
ValueCountFrequency (%)
55 1
 
< 0.1%
50 1
 
< 0.1%
40 1
 
< 0.1%
27 2
 
< 0.1%
26 5
 
< 0.1%
20 2
 
< 0.1%
10 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 62
0.1%

children
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size931.3 KiB
0.0
110614 
1.0
 
4855
2.0
 
3640
3.0
 
76
10.0
 
1

Length

Max length4
Median length3
Mean length3.0000084
Min length3

Characters and Unicode

Total characters357559
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 110614
92.8%
1.0 4855
 
4.1%
2.0 3640
 
3.1%
3.0 76
 
0.1%
10.0 1
 
< 0.1%
(Missing) 4
 
< 0.1%

Length

2025-01-03T18:52:34.435200image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:34.795679image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 110614
92.8%
1.0 4855
 
4.1%
2.0 3640
 
3.1%
3.0 76
 
0.1%
10.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 229801
64.3%
. 119186
33.3%
1 4856
 
1.4%
2 3640
 
1.0%
3 76
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 357559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 229801
64.3%
. 119186
33.3%
1 4856
 
1.4%
2 3640
 
1.0%
3 76
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 357559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 229801
64.3%
. 119186
33.3%
1 4856
 
1.4%
2 3640
 
1.0%
3 76
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 357559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 229801
64.3%
. 119186
33.3%
1 4856
 
1.4%
2 3640
 
1.0%
3 76
 
< 0.1%

babies
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
0
118274 
1
 
899
2
 
15
9
 
1
10
 
1

Length

Max length2
Median length1
Mean length1.0000084
Min length1

Characters and Unicode

Total characters119191
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 118274
99.2%
1 899
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%

Length

2025-01-03T18:52:35.171140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:35.543748image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 118274
99.2%
1 899
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 118275
99.2%
1 900
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119191
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 118275
99.2%
1 900
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119191
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 118275
99.2%
1 900
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119191
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 118275
99.2%
1 900
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

meal
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
BB
92160 
HB
14434 
SC
10631 
Undefined
 
1169
FB
 
796

Length

Max length9
Median length2
Mean length2.0686551
Min length2

Characters and Unicode

Total characters246563
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowSC
3rd rowSC
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 92160
77.3%
HB 14434
 
12.1%
SC 10631
 
8.9%
Undefined 1169
 
1.0%
FB 796
 
0.7%

Length

2025-01-03T18:52:36.004164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:36.330311image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
bb 92160
77.3%
hb 14434
 
12.1%
sc 10631
 
8.9%
undefined 1169
 
1.0%
fb 796
 
0.7%

Most occurring characters

ValueCountFrequency (%)
B 199550
80.9%
H 14434
 
5.9%
S 10631
 
4.3%
C 10631
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 246563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 199550
80.9%
H 14434
 
5.9%
S 10631
 
4.3%
C 10631
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 246563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 199550
80.9%
H 14434
 
5.9%
S 10631
 
4.3%
C 10631
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 246563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 199550
80.9%
H 14434
 
5.9%
S 10631
 
4.3%
C 10631
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%
Distinct177
Distinct (%)0.1%
Missing487
Missing (%)0.4%
Memory size931.3 KiB
2025-01-03T18:52:38.294794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9892505
Min length2

Characters and Unicode

Total characters354833
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)< 0.1%

Sample

1st rowBEL
2nd rowDEU
3rd rowESP
4th rowPRT
5th rowPRT
ValueCountFrequency (%)
prt 48511
40.9%
gbr 12113
 
10.2%
fra 10400
 
8.8%
esp 8549
 
7.2%
deu 7273
 
6.1%
ita 3760
 
3.2%
irl 3372
 
2.8%
bel 2337
 
2.0%
bra 2219
 
1.9%
nld 2102
 
1.8%
Other values (167) 18067
 
15.2%
2025-01-03T18:52:40.331276image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 80676
22.7%
P 58405
16.5%
T 54175
15.3%
A 21590
 
6.1%
E 21495
 
6.1%
B 17025
 
4.8%
S 13900
 
3.9%
U 13268
 
3.7%
G 13112
 
3.7%
F 10940
 
3.1%
Other values (16) 50247
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 354833
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 80676
22.7%
P 58405
16.5%
T 54175
15.3%
A 21590
 
6.1%
E 21495
 
6.1%
B 17025
 
4.8%
S 13900
 
3.9%
U 13268
 
3.7%
G 13112
 
3.7%
F 10940
 
3.1%
Other values (16) 50247
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 354833
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 80676
22.7%
P 58405
16.5%
T 54175
15.3%
A 21590
 
6.1%
E 21495
 
6.1%
B 17025
 
4.8%
S 13900
 
3.9%
U 13268
 
3.7%
G 13112
 
3.7%
F 10940
 
3.1%
Other values (16) 50247
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 354833
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 80676
22.7%
P 58405
16.5%
T 54175
15.3%
A 21590
 
6.1%
E 21495
 
6.1%
B 17025
 
4.8%
S 13900
 
3.9%
U 13268
 
3.7%
G 13112
 
3.7%
F 10940
 
3.1%
Other values (16) 50247
14.2%

market_segment
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
Online TA
56392 
Offline TA/TO
24170 
Groups
19777 
Direct
12581 
Corporate
 
5292
Other values (3)
 
978

Length

Max length13
Median length9
Mean length9.0195906
Min length6

Characters and Unicode

Total characters1075045
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOnline TA
2nd rowOnline TA
3rd rowOnline TA
4th rowDirect
5th rowDirect

Common Values

ValueCountFrequency (%)
Online TA 56392
47.3%
Offline TA/TO 24170
20.3%
Groups 19777
 
16.6%
Direct 12581
 
10.6%
Corporate 5292
 
4.4%
Complementary 741
 
0.6%
Aviation 235
 
0.2%
Undefined 2
 
< 0.1%

Length

2025-01-03T18:52:40.616070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:41.023487image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
online 56392
28.2%
ta 56392
28.2%
offline 24170
12.1%
ta/to 24170
12.1%
groups 19777
 
9.9%
direct 12581
 
6.3%
corporate 5292
 
2.6%
complementary 741
 
0.4%
aviation 235
 
0.1%
undefined 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 137934
12.8%
O 104732
9.7%
T 104732
9.7%
e 99921
9.3%
i 93615
8.7%
l 81303
7.6%
A 80797
7.5%
80562
7.5%
f 48342
 
4.5%
r 43683
 
4.1%
Other values (16) 199424
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1075045
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 137934
12.8%
O 104732
9.7%
T 104732
9.7%
e 99921
9.3%
i 93615
8.7%
l 81303
7.6%
A 80797
7.5%
80562
7.5%
f 48342
 
4.5%
r 43683
 
4.1%
Other values (16) 199424
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1075045
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 137934
12.8%
O 104732
9.7%
T 104732
9.7%
e 99921
9.3%
i 93615
8.7%
l 81303
7.6%
A 80797
7.5%
80562
7.5%
f 48342
 
4.5%
r 43683
 
4.1%
Other values (16) 199424
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1075045
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 137934
12.8%
O 104732
9.7%
T 104732
9.7%
e 99921
9.3%
i 93615
8.7%
l 81303
7.6%
A 80797
7.5%
80562
7.5%
f 48342
 
4.5%
r 43683
 
4.1%
Other values (16) 199424
18.6%

distribution_channel
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
TA/TO
97706 
Direct
14616 
Corporate
 
6670
GDS
 
193
Undefined
 
5

Length

Max length9
Median length5
Mean length5.3434013
Min length3

Characters and Unicode

Total characters636880
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA/TO
2nd rowTA/TO
3rd rowTA/TO
4th rowDirect
5th rowDirect

Common Values

ValueCountFrequency (%)
TA/TO 97706
82.0%
Direct 14616
 
12.3%
Corporate 6670
 
5.6%
GDS 193
 
0.2%
Undefined 5
 
< 0.1%

Length

2025-01-03T18:52:41.630811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:41.967877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 97706
82.0%
direct 14616
 
12.3%
corporate 6670
 
5.6%
gds 193
 
0.2%
undefined 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 195412
30.7%
/ 97706
15.3%
O 97706
15.3%
A 97706
15.3%
r 27956
 
4.4%
e 21296
 
3.3%
t 21286
 
3.3%
D 14809
 
2.3%
i 14621
 
2.3%
c 14616
 
2.3%
Other values (10) 33766
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 636880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 195412
30.7%
/ 97706
15.3%
O 97706
15.3%
A 97706
15.3%
r 27956
 
4.4%
e 21296
 
3.3%
t 21286
 
3.3%
D 14809
 
2.3%
i 14621
 
2.3%
c 14616
 
2.3%
Other values (10) 33766
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 636880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 195412
30.7%
/ 97706
15.3%
O 97706
15.3%
A 97706
15.3%
r 27956
 
4.4%
e 21296
 
3.3%
t 21286
 
3.3%
D 14809
 
2.3%
i 14621
 
2.3%
c 14616
 
2.3%
Other values (10) 33766
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 636880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 195412
30.7%
/ 97706
15.3%
O 97706
15.3%
A 97706
15.3%
r 27956
 
4.4%
e 21296
 
3.3%
t 21286
 
3.3%
D 14809
 
2.3%
i 14621
 
2.3%
c 14616
 
2.3%
Other values (10) 33766
 
5.3%

is_repeated_guest
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
0
115383 
1
 
3807

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119190
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 115383
96.8%
1 3807
 
3.2%

Length

2025-01-03T18:52:42.366467image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:42.636197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 115383
96.8%
1 3807
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 115383
96.8%
1 3807
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 115383
96.8%
1 3807
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 115383
96.8%
1 3807
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 115383
96.8%
1 3807
 
3.2%

previous_cancellations
Real number (ℝ)

Skewed  Zeros 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087205302
Minimum0
Maximum26
Zeros112713
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:42.882929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84500825
Coefficient of variation (CV)9.6898724
Kurtosis673.04527
Mean0.087205302
Median Absolute Deviation (MAD)0
Skewness24.440316
Sum10394
Variance0.71403895
MonotonicityNot monotonic
2025-01-03T18:52:43.108539image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 112713
94.6%
1 6044
 
5.1%
2 116
 
0.1%
3 65
 
0.1%
24 48
 
< 0.1%
11 35
 
< 0.1%
4 31
 
< 0.1%
26 26
 
< 0.1%
25 25
 
< 0.1%
6 22
 
< 0.1%
Other values (5) 65
 
0.1%
ValueCountFrequency (%)
0 112713
94.6%
1 6044
 
5.1%
2 116
 
0.1%
3 65
 
0.1%
4 31
 
< 0.1%
5 19
 
< 0.1%
6 22
 
< 0.1%
11 35
 
< 0.1%
13 12
 
< 0.1%
14 14
 
< 0.1%
ValueCountFrequency (%)
26 26
< 0.1%
25 25
< 0.1%
24 48
< 0.1%
21 1
 
< 0.1%
19 19
 
< 0.1%
14 14
 
< 0.1%
13 12
 
< 0.1%
11 35
< 0.1%
6 22
< 0.1%
5 19
 
< 0.1%

previous_bookings_not_canceled
Real number (ℝ)

Skewed  Zeros 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13717594
Minimum0
Maximum72
Zeros115573
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:43.364976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4979736
Coefficient of variation (CV)10.92009
Kurtosis767.32199
Mean0.13717594
Median Absolute Deviation (MAD)0
Skewness23.543838
Sum16350
Variance2.243925
MonotonicityNot monotonic
2025-01-03T18:52:43.689633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115573
97.0%
1 1540
 
1.3%
2 580
 
0.5%
3 333
 
0.3%
4 229
 
0.2%
5 181
 
0.2%
6 115
 
0.1%
7 88
 
0.1%
8 70
 
0.1%
9 60
 
0.1%
Other values (63) 421
 
0.4%
ValueCountFrequency (%)
0 115573
97.0%
1 1540
 
1.3%
2 580
 
0.5%
3 333
 
0.3%
4 229
 
0.2%
5 181
 
0.2%
6 115
 
0.1%
7 88
 
0.1%
8 70
 
0.1%
9 60
 
0.1%
ValueCountFrequency (%)
72 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%

reserved_room_type
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
A
85847 
D
19176 
E
 
6521
F
 
2891
G
 
2091
Other values (5)
 
2664

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119190
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
A 85847
72.0%
D 19176
 
16.1%
E 6521
 
5.5%
F 2891
 
2.4%
G 2091
 
1.8%
B 1116
 
0.9%
C 930
 
0.8%
H 600
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Length

2025-01-03T18:52:44.052540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:44.418061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
a 85847
72.0%
d 19176
 
16.1%
e 6521
 
5.5%
f 2891
 
2.4%
g 2091
 
1.8%
b 1116
 
0.9%
c 930
 
0.8%
h 600
 
0.5%
p 12
 
< 0.1%
l 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 85847
72.0%
D 19176
 
16.1%
E 6521
 
5.5%
F 2891
 
2.4%
G 2091
 
1.8%
B 1116
 
0.9%
C 930
 
0.8%
H 600
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 85847
72.0%
D 19176
 
16.1%
E 6521
 
5.5%
F 2891
 
2.4%
G 2091
 
1.8%
B 1116
 
0.9%
C 930
 
0.8%
H 600
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 85847
72.0%
D 19176
 
16.1%
E 6521
 
5.5%
F 2891
 
2.4%
G 2091
 
1.8%
B 1116
 
0.9%
C 930
 
0.8%
H 600
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 85847
72.0%
D 19176
 
16.1%
E 6521
 
5.5%
F 2891
 
2.4%
G 2091
 
1.8%
B 1116
 
0.9%
C 930
 
0.8%
H 600
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

assigned_room_type
Categorical

High correlation  Imbalance 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
A
73924 
D
25287 
E
7790 
F
 
3744
G
 
2548
Other values (7)
 
5897

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119190
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
A 73924
62.0%
D 25287
 
21.2%
E 7790
 
6.5%
F 3744
 
3.1%
G 2548
 
2.1%
C 2373
 
2.0%
B 2160
 
1.8%
H 710
 
0.6%
I 362
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Length

2025-01-03T18:52:44.826861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 73924
62.0%
d 25287
 
21.2%
e 7790
 
6.5%
f 3744
 
3.1%
g 2548
 
2.1%
c 2373
 
2.0%
b 2160
 
1.8%
h 710
 
0.6%
i 362
 
0.3%
k 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 73924
62.0%
D 25287
 
21.2%
E 7790
 
6.5%
F 3744
 
3.1%
G 2548
 
2.1%
C 2373
 
2.0%
B 2160
 
1.8%
H 710
 
0.6%
I 362
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 73924
62.0%
D 25287
 
21.2%
E 7790
 
6.5%
F 3744
 
3.1%
G 2548
 
2.1%
C 2373
 
2.0%
B 2160
 
1.8%
H 710
 
0.6%
I 362
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 73924
62.0%
D 25287
 
21.2%
E 7790
 
6.5%
F 3744
 
3.1%
G 2548
 
2.1%
C 2373
 
2.0%
B 2160
 
1.8%
H 710
 
0.6%
I 362
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 73924
62.0%
D 25287
 
21.2%
E 7790
 
6.5%
F 3744
 
3.1%
G 2548
 
2.1%
C 2373
 
2.0%
B 2160
 
1.8%
H 710
 
0.6%
I 362
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

booking_changes
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22105881
Minimum0
Maximum21
Zeros101147
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:45.076694image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.65188303
Coefficient of variation (CV)2.9489122
Kurtosis79.316679
Mean0.22105881
Median Absolute Deviation (MAD)0
Skewness5.9917234
Sum26348
Variance0.42495148
MonotonicityNot monotonic
2025-01-03T18:52:45.314521image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 101147
84.9%
1 12678
 
10.6%
2 3797
 
3.2%
3 926
 
0.8%
4 376
 
0.3%
5 118
 
0.1%
6 63
 
0.1%
7 31
 
< 0.1%
8 17
 
< 0.1%
9 8
 
< 0.1%
Other values (11) 29
 
< 0.1%
ValueCountFrequency (%)
0 101147
84.9%
1 12678
 
10.6%
2 3797
 
3.2%
3 926
 
0.8%
4 376
 
0.3%
5 118
 
0.1%
6 63
 
0.1%
7 31
 
< 0.1%
8 17
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 3
< 0.1%
14 5
< 0.1%
13 5
< 0.1%
12 2
 
< 0.1%
11 2
 
< 0.1%

deposit_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
No Deposit
104466 
Non Refund
14563 
Refundable
 
161

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1191900
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 104466
87.6%
Non Refund 14563
 
12.2%
Refundable 161
 
0.1%

Length

2025-01-03T18:52:45.995169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:46.342479image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
no 104466
43.9%
deposit 104466
43.9%
non 14563
 
6.1%
refund 14563
 
6.1%
refundable 161
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 223495
18.8%
e 119351
10.0%
N 119029
10.0%
119029
10.0%
s 104466
8.8%
i 104466
8.8%
t 104466
8.8%
p 104466
8.8%
D 104466
8.8%
n 29287
 
2.5%
Other values (7) 59379
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1191900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 223495
18.8%
e 119351
10.0%
N 119029
10.0%
119029
10.0%
s 104466
8.8%
i 104466
8.8%
t 104466
8.8%
p 104466
8.8%
D 104466
8.8%
n 29287
 
2.5%
Other values (7) 59379
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1191900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 223495
18.8%
e 119351
10.0%
N 119029
10.0%
119029
10.0%
s 104466
8.8%
i 104466
8.8%
t 104466
8.8%
p 104466
8.8%
D 104466
8.8%
n 29287
 
2.5%
Other values (7) 59379
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1191900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 223495
18.8%
e 119351
10.0%
N 119029
10.0%
119029
10.0%
s 104466
8.8%
i 104466
8.8%
t 104466
8.8%
p 104466
8.8%
D 104466
8.8%
n 29287
 
2.5%
Other values (7) 59379
 
5.0%

agent
Real number (ℝ)

High correlation  Missing 

Distinct333
Distinct (%)0.3%
Missing16315
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean86.680389
Minimum1
Maximum535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:46.689063image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median14
Q3229
95-th percentile250
Maximum535
Range534
Interquartile range (IQR)220

Descriptive statistics

Standard deviation110.7661
Coefficient of variation (CV)1.277868
Kurtosis-0.0085158428
Mean86.680389
Median Absolute Deviation (MAD)13
Skewness1.0892704
Sum8917245
Variance12269.128
MonotonicityNot monotonic
2025-01-03T18:52:47.090337image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 31915
26.8%
240 13901
11.7%
1 7182
 
6.0%
14 3635
 
3.0%
7 3533
 
3.0%
6 3283
 
2.8%
250 2863
 
2.4%
241 1718
 
1.4%
28 1663
 
1.4%
8 1509
 
1.3%
Other values (323) 31673
26.6%
(Missing) 16315
13.7%
ValueCountFrequency (%)
1 7182
 
6.0%
2 161
 
0.1%
3 1333
 
1.1%
4 47
 
< 0.1%
5 330
 
0.3%
6 3283
 
2.8%
7 3533
 
3.0%
8 1509
 
1.3%
9 31915
26.8%
10 260
 
0.2%
ValueCountFrequency (%)
535 3
 
< 0.1%
531 68
0.1%
527 35
< 0.1%
526 8
 
< 0.1%
510 2
 
< 0.1%
509 10
 
< 0.1%
508 6
 
< 0.1%
502 24
 
< 0.1%
497 1
 
< 0.1%
495 57
< 0.1%

company
Real number (ℝ)

Missing 

Distinct352
Distinct (%)5.2%
Missing112402
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean189.1868
Minimum6
Maximum543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:47.400572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile40
Q162
median178
Q3270
95-th percentile435
Maximum543
Range537
Interquartile range (IQR)208

Descriptive statistics

Standard deviation131.69555
Coefficient of variation (CV)0.69611383
Kurtosis-0.49024666
Mean189.1868
Median Absolute Deviation (MAD)110
Skewness0.6028196
Sum1284200
Variance17343.718
MonotonicityNot monotonic
2025-01-03T18:52:47.679041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 927
 
0.8%
223 782
 
0.7%
67 267
 
0.2%
45 250
 
0.2%
153 213
 
0.2%
174 149
 
0.1%
219 141
 
0.1%
281 137
 
0.1%
154 133
 
0.1%
405 119
 
0.1%
Other values (342) 3670
 
3.1%
(Missing) 112402
94.3%
ValueCountFrequency (%)
6 1
 
< 0.1%
8 1
 
< 0.1%
9 37
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 14
 
< 0.1%
14 9
 
< 0.1%
16 5
 
< 0.1%
18 1
 
< 0.1%
20 50
< 0.1%
ValueCountFrequency (%)
543 2
 
< 0.1%
541 1
 
< 0.1%
539 2
 
< 0.1%
534 2
 
< 0.1%
531 1
 
< 0.1%
530 5
 
< 0.1%
528 2
 
< 0.1%
525 15
< 0.1%
523 19
< 0.1%
521 7
 
< 0.1%

days_in_waiting_list
Real number (ℝ)

Zeros 

Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.316243
Minimum0
Maximum391
Zeros115501
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:48.006483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.566855
Coefficient of variation (CV)7.5842023
Kurtosis187.6359
Mean2.316243
Median Absolute Deviation (MAD)0
Skewness11.965906
Sum276073
Variance308.59441
MonotonicityNot monotonic
2025-01-03T18:52:48.350791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115501
96.9%
39 226
 
0.2%
58 164
 
0.1%
44 141
 
0.1%
31 127
 
0.1%
35 96
 
0.1%
46 94
 
0.1%
69 89
 
0.1%
63 83
 
0.1%
87 80
 
0.1%
Other values (118) 2589
 
2.2%
ValueCountFrequency (%)
0 115501
96.9%
1 12
 
< 0.1%
2 5
 
< 0.1%
3 59
 
< 0.1%
4 25
 
< 0.1%
5 8
 
< 0.1%
6 16
 
< 0.1%
7 4
 
< 0.1%
8 7
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
391 45
< 0.1%
379 15
 
< 0.1%
330 15
 
< 0.1%
259 10
 
< 0.1%
236 34
< 0.1%
224 10
 
< 0.1%
223 59
< 0.1%
215 21
 
< 0.1%
207 15
 
< 0.1%
193 1
 
< 0.1%

customer_type
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
Transient
89466 
Transient-Party
25072 
Contract
 
4075
Group
 
577

Length

Max length15
Median length9
Mean length10.208566
Min length5

Characters and Unicode

Total characters1216759
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 89466
75.1%
Transient-Party 25072
 
21.0%
Contract 4075
 
3.4%
Group 577
 
0.5%

Length

2025-01-03T18:52:48.887483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:49.346671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
transient 89466
75.1%
transient-party 25072
 
21.0%
contract 4075
 
3.4%
group 577
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 233151
19.2%
t 147760
12.1%
r 144262
11.9%
a 143685
11.8%
T 114538
9.4%
s 114538
9.4%
i 114538
9.4%
e 114538
9.4%
y 25072
 
2.1%
- 25072
 
2.1%
Other values (7) 39605
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1216759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 233151
19.2%
t 147760
12.1%
r 144262
11.9%
a 143685
11.8%
T 114538
9.4%
s 114538
9.4%
i 114538
9.4%
e 114538
9.4%
y 25072
 
2.1%
- 25072
 
2.1%
Other values (7) 39605
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1216759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 233151
19.2%
t 147760
12.1%
r 144262
11.9%
a 143685
11.8%
T 114538
9.4%
s 114538
9.4%
i 114538
9.4%
e 114538
9.4%
y 25072
 
2.1%
- 25072
 
2.1%
Other values (7) 39605
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1216759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 233151
19.2%
t 147760
12.1%
r 144262
11.9%
a 143685
11.8%
T 114538
9.4%
s 114538
9.4%
i 114538
9.4%
e 114538
9.4%
y 25072
 
2.1%
- 25072
 
2.1%
Other values (7) 39605
 
3.3%

adr
Real number (ℝ)

Zeros 

Distinct8871
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.82772
Minimum-6.38
Maximum5400
Zeros1953
Zeros (%)1.6%
Negative1
Negative (%)< 0.1%
Memory size931.3 KiB
2025-01-03T18:52:49.732531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile38.4
Q169.2
median94.5
Q3126
95-th percentile193.5
Maximum5400
Range5406.38
Interquartile range (IQR)56.8

Descriptive statistics

Standard deviation50.537121
Coefficient of variation (CV)0.49630024
Kurtosis1014.7826
Mean101.82772
Median Absolute Deviation (MAD)27.9
Skewness10.545542
Sum12136846
Variance2554.0006
MonotonicityNot monotonic
2025-01-03T18:52:50.017312image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 3752
 
3.1%
75 2710
 
2.3%
90 2470
 
2.1%
65 2414
 
2.0%
0 1953
 
1.6%
80 1883
 
1.6%
95 1658
 
1.4%
120 1603
 
1.3%
100 1572
 
1.3%
85 1537
 
1.3%
Other values (8861) 97638
81.9%
ValueCountFrequency (%)
-6.38 1
 
< 0.1%
0 1953
1.6%
0.26 1
 
< 0.1%
0.5 1
 
< 0.1%
1 15
 
< 0.1%
1.29 1
 
< 0.1%
1.48 1
 
< 0.1%
1.56 2
 
< 0.1%
1.6 1
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
5400 1
< 0.1%
510 1
< 0.1%
508 1
< 0.1%
451.5 1
< 0.1%
450 1
< 0.1%
437 1
< 0.1%
426.25 1
< 0.1%
402 1
< 0.1%
397.38 1
< 0.1%
392 2
< 0.1%

required_car_parking_spaces
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
0
111793 
1
 
7365
2
 
27
3
 
3
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119190
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 111793
93.8%
1 7365
 
6.2%
2 27
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Length

2025-01-03T18:52:50.337041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:50.666955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 111793
93.8%
1 7365
 
6.2%
2 27
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111793
93.8%
1 7365
 
6.2%
2 27
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111793
93.8%
1 7365
 
6.2%
2 27
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111793
93.8%
1 7365
 
6.2%
2 27
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111793
93.8%
1 7365
 
6.2%
2 27
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

total_of_special_requests
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57139022
Minimum0
Maximum5
Zeros70203
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size931.3 KiB
2025-01-03T18:52:50.955856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79287386
Coefficient of variation (CV)1.3876224
Kurtosis1.4918075
Mean0.57139022
Median Absolute Deviation (MAD)0
Skewness1.349148
Sum68104
Variance0.62864896
MonotonicityNot monotonic
2025-01-03T18:52:51.202871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 70203
58.9%
1 33163
27.8%
2 12950
 
10.9%
3 2495
 
2.1%
4 339
 
0.3%
5 40
 
< 0.1%
ValueCountFrequency (%)
0 70203
58.9%
1 33163
27.8%
2 12950
 
10.9%
3 2495
 
2.1%
4 339
 
0.3%
5 40
 
< 0.1%
ValueCountFrequency (%)
5 40
 
< 0.1%
4 339
 
0.3%
3 2495
 
2.1%
2 12950
 
10.9%
1 33163
27.8%
0 70203
58.9%

reservation_status
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
Check-Out
75039 
Canceled
42947 
No-Show
 
1204

Length

Max length9
Median length9
Mean length8.6194731
Min length7

Characters and Unicode

Total characters1027355
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCanceled
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out 75039
63.0%
Canceled 42947
36.0%
No-Show 1204
 
1.0%

Length

2025-01-03T18:52:51.520719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T18:52:51.906651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
check-out 75039
63.0%
canceled 42947
36.0%
no-show 1204
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 160933
15.7%
C 117986
11.5%
c 117986
11.5%
h 76243
7.4%
- 76243
7.4%
u 75039
7.3%
t 75039
7.3%
O 75039
7.3%
k 75039
7.3%
a 42947
 
4.2%
Other values (7) 134861
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1027355
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 160933
15.7%
C 117986
11.5%
c 117986
11.5%
h 76243
7.4%
- 76243
7.4%
u 75039
7.3%
t 75039
7.3%
O 75039
7.3%
k 75039
7.3%
a 42947
 
4.2%
Other values (7) 134861
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1027355
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 160933
15.7%
C 117986
11.5%
c 117986
11.5%
h 76243
7.4%
- 76243
7.4%
u 75039
7.3%
t 75039
7.3%
O 75039
7.3%
k 75039
7.3%
a 42947
 
4.2%
Other values (7) 134861
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1027355
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 160933
15.7%
C 117986
11.5%
c 117986
11.5%
h 76243
7.4%
- 76243
7.4%
u 75039
7.3%
t 75039
7.3%
O 75039
7.3%
k 75039
7.3%
a 42947
 
4.2%
Other values (7) 134861
13.1%
Distinct926
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size931.3 KiB
Minimum2014-10-17 00:00:00
Maximum2017-09-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-03T18:52:52.243940image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:52.707978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-01-03T18:52:17.410933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:24.065916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:28.122871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:32.257836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:36.088135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:40.627147image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:45.310875image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:49.371239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:53.163691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:57.470809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:01.180068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:04.838010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:08.960583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:13.427202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:17.704837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:24.449006image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:28.407862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:32.560735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:36.453918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:40.925916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:45.773069image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:49.638642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:53.462432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:57.731970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:01.424489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:05.073500image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:09.293772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:13.689057image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:17.951838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:24.721224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:28.645284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:32.862800image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:36.764766image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:41.222176image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:46.081090image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:49.911278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:53.737348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:58.001837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:01.695823image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:05.312133image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:09.590130image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:13.989921image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:18.228432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:25.001025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:28.920891image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:33.109919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:37.107330image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:41.524021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:46.377065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:50.207299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:54.042563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:58.288929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:01.964827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:05.600955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:09.914117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:14.296465image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:18.757010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:25.373278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:29.230832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:33.396927image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:37.405180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:41.871711image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:46.693053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:50.541948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:54.388836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:58.575902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:02.224905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:05.973780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:10.328416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:14.652693image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:19.036947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:25.671248image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:29.487974image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:33.663215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:37.676936image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:42.125288image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:47.017826image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:50.818097image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:54.921522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:58.826791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:02.442220image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:06.495179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:10.635607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:14.930973image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:19.332369image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:25.952040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:29.739809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:33.907660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:37.927001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:42.592571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:47.285405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:51.040796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:55.178407image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:59.057017image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:02.688859image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:06.778758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:10.958131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:15.243093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:19.643260image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:26.216503image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:30.021405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:34.189867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:38.220267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:42.975680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:47.541736image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:51.265907image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:55.451576image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:59.293356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:02.931238image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:07.021742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:11.262274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:15.528261image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:19.930720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:26.506712image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:30.323402image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:34.516384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:38.553119image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:43.329273image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:47.853251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:51.533706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:55.723139image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:59.583634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:03.221682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:07.349260image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:11.592310image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:15.795740image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:20.175549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:26.759605image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:30.601690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:34.772119image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:38.865699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:43.608201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:48.083115image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:51.755450image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:55.980453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:59.804497image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:03.540611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:07.567194image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:11.870486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:16.047475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:20.416380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:27.017897image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:31.152956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:35.043264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:39.182065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:43.888542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:48.346067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:51.991347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:56.231618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:00.063197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:03.776199image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:07.814955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:12.151139image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:16.279131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:20.715549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:27.259960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:31.397403image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:35.291294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:39.583920image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:44.205819image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:48.642148image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:52.273132image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:56.511454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:00.339437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:04.079397image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:08.051603image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:12.458213image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:16.514101image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:21.004111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:27.559870image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:31.673541image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:35.582591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:39.953161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:44.602050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:48.915105image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:52.651685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:56.860971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:00.676354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:04.361382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:08.339675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:12.791989image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:16.840364image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:21.307493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:27.848734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:31.956066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:35.830041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:40.288600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:44.938936image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:49.134022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:52.918656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:51:57.165948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:00.913185image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:04.603323image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:08.607749image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:13.127844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-03T18:52:17.110547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-01-03T18:52:53.013445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
adradultsagentarrival_date_day_of_montharrival_date_montharrival_date_week_numberarrival_date_yearassigned_room_typebabiesbooking_changeschildrencompanycustomer_typedays_in_waiting_listdeposit_typedistribution_channelhotelis_canceledis_repeated_guestlead_timemarket_segmentmealprevious_bookings_not_canceledprevious_cancellationsrequired_car_parking_spacesreservation_statusreserved_room_typestays_in_week_nightsstays_in_weekend_nightstotal_of_special_requests
adr1.0000.280-0.0490.0270.0010.0740.0000.0000.0000.0050.0000.0520.000-0.0390.0070.0000.0000.0000.0000.0150.0000.000-0.143-0.1500.0000.0000.0000.0940.0510.196
adults0.2801.000-0.0560.0020.0100.0260.0150.0000.000-0.0850.0000.2310.089-0.0370.0000.0080.0140.0130.0000.1920.0080.000-0.210-0.0360.0000.0080.0030.1530.1270.162
agent-0.049-0.0561.0000.0050.083-0.0570.0910.1330.0260.0910.0580.2260.125-0.0190.1190.2090.8170.0860.076-0.1240.2220.1850.060-0.1680.1310.0640.1430.1710.1310.016
arrival_date_day_of_month0.0270.0020.0051.0000.0580.0610.0440.0090.0050.0130.0100.0450.0320.0320.0540.0280.0260.0210.0180.0080.0330.039-0.002-0.0120.0070.0230.010-0.016-0.0070.003
arrival_date_month0.0010.0100.0830.0581.0000.8010.4290.0270.0160.0100.0690.2170.1030.0600.1010.0680.0700.0690.0750.1320.0880.0890.0170.0320.0180.0650.0450.0370.0460.053
arrival_date_week_number0.0740.026-0.0570.0610.8011.0000.4240.0280.0140.0080.062-0.0580.106-0.0040.0950.0640.0670.0660.0760.1120.0810.080-0.0430.0870.0170.0610.0420.0260.0260.019
arrival_date_year0.0000.0150.0910.0440.4290.4241.0000.0530.0090.0160.0440.2810.2140.0740.0520.0270.0430.0260.0100.1040.1590.1120.0250.0520.0180.0230.0820.0140.0290.091
assigned_room_type0.0000.0000.1330.0090.0270.0280.0531.0000.0440.0520.3040.0850.0900.0290.1920.0950.3910.2030.0710.0620.1210.1160.0030.0080.0920.1450.7760.0470.0510.066
babies0.0000.0000.0260.0050.0160.0140.0090.0441.0000.0170.0250.0320.0150.0000.0230.0290.0490.0340.0070.0070.0340.0160.0000.0000.0200.0240.0400.0000.0100.060
booking_changes0.005-0.0850.0910.0130.0100.0080.0160.0520.0171.0000.0170.1760.028-0.0190.0290.0270.0400.0480.000-0.0080.0200.0100.031-0.0730.0170.0340.0140.0650.0400.042
children0.0000.0000.0580.0100.0690.0620.0440.3040.0250.0171.0000.0390.0610.0180.0730.0430.0460.0280.0350.0280.1000.0370.0020.0000.0300.0280.3570.0130.0280.061
company0.0520.2310.2260.0450.217-0.0580.2810.0850.0320.1760.0391.0000.2510.0210.1830.2180.4980.1410.3580.2860.3920.200-0.298-0.1980.0470.1060.0980.2500.076-0.128
customer_type0.0000.0890.1250.0320.1030.1060.2140.0900.0150.0280.0610.2511.0000.0780.0980.0790.0520.1360.1050.1220.2760.1390.0140.0100.0410.0970.1090.0800.0880.097
days_in_waiting_list-0.039-0.037-0.0190.0320.060-0.0040.0740.0290.000-0.0190.0180.0210.0781.0000.1270.0270.0870.0670.0240.1530.0780.061-0.0190.1160.0340.0500.0280.012-0.075-0.123
deposit_type0.0070.0000.1190.0540.1010.0950.0520.1920.0230.0290.0730.1830.0980.1271.0000.0910.1760.4810.0580.2740.3740.0930.0130.0510.0710.3470.1520.0470.0730.220
distribution_channel0.0000.0080.2090.0280.0680.0640.0270.0950.0290.0270.0430.2180.0790.0270.0911.0000.1870.1770.2970.1160.6920.0770.1080.0510.0760.1290.1000.0060.0550.070
hotel0.0000.0140.8170.0260.0700.0670.0430.3910.0490.0400.0460.4980.0520.0870.1760.1871.0000.1360.0500.0940.1470.3170.0170.0500.2200.1360.3230.1920.1980.046
is_canceled0.0000.0130.0860.0210.0690.0660.0260.2030.0340.0480.0280.1410.1360.0670.4810.1770.1361.0000.0850.2810.2670.0500.0410.0440.1971.0000.0730.0280.0220.265
is_repeated_guest0.0000.0000.0760.0180.0750.0760.0100.0710.0070.0000.0350.3580.1050.0240.0580.2970.0500.0851.0000.1340.3470.0610.3200.1850.0780.0860.0370.0170.0820.040
lead_time0.0150.192-0.1240.0080.1320.1120.1040.0620.007-0.0080.0280.2860.1220.1530.2740.1160.0940.2810.1341.0000.1700.089-0.1890.1710.0570.2070.0480.2960.162-0.074
market_segment0.0000.0080.2220.0330.0880.0810.1590.1210.0340.0200.1000.3920.2760.0780.3740.6920.1470.2670.3470.1701.0000.1910.0970.0540.0920.1950.1380.0330.0610.210
meal0.0000.0000.1850.0390.0890.0800.1120.1160.0160.0100.0370.2000.1390.0610.0930.0770.3170.0500.0610.0890.1911.0000.0140.0880.0260.0400.1030.0450.0610.062
previous_bookings_not_canceled-0.143-0.2100.060-0.0020.017-0.0430.0250.0030.0000.0310.002-0.2980.014-0.0190.0130.1080.0170.0410.320-0.1890.0970.0141.0000.1020.0190.0290.003-0.119-0.0840.025
previous_cancellations-0.150-0.036-0.168-0.0120.0320.0870.0520.0080.000-0.0730.000-0.1980.0100.1160.0510.0510.0500.0440.1850.1710.0540.0880.1021.0000.0000.0310.006-0.062-0.055-0.130
required_car_parking_spaces0.0000.0000.1310.0070.0180.0170.0180.0920.0200.0170.0300.0470.0410.0340.0710.0760.2200.1970.0780.0570.0920.0260.0190.0001.0000.1390.0790.0170.0150.044
reservation_status0.0000.0080.0640.0230.0650.0610.0230.1450.0240.0340.0280.1060.0970.0500.3470.1290.1361.0000.0860.2070.1950.0400.0290.0310.1391.0000.0520.0300.0240.189
reserved_room_type0.0000.0030.1430.0100.0450.0420.0820.7760.0400.0140.3570.0980.1090.0280.1520.1000.3230.0730.0370.0480.1380.1030.0030.0060.0790.0521.0000.0440.0540.075
stays_in_week_nights0.0940.1530.171-0.0160.0370.0260.0140.0470.0000.0650.0130.2500.0800.0120.0470.0060.1920.0280.0170.2960.0330.045-0.119-0.0620.0170.0300.0441.0000.2380.076
stays_in_weekend_nights0.0510.1270.131-0.0070.0460.0260.0290.0510.0100.0400.0280.0760.088-0.0750.0730.0550.1980.0220.0820.1620.0610.061-0.084-0.0550.0150.0240.0540.2381.0000.080
total_of_special_requests0.1960.1620.0160.0030.0530.0190.0910.0660.0600.0420.061-0.1280.097-0.1230.2200.0700.0460.2650.040-0.0740.2100.0620.025-0.1300.0440.1890.0750.0760.0801.000

Missing values

2025-01-03T18:52:21.769095image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-03T18:52:24.193899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-03T18:52:27.430461image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
0City Hotel0212015September3610420.00BBBELOnline TATA/TO000AA2No Deposit9.0NaN0Transient105.000Check-Out2015-09-05
1City Hotel0202016September38121010.00SCDEUOnline TATA/TO000AA0No Deposit9.0NaN0Transient89.002Check-Out2016-09-13
2City Hotel022016March13240220.00SCESPOnline TATA/TO000AA0No Deposit9.0NaN0Transient134.001Check-Out2016-03-26
3Resort Hotel162016April17210120.00BBPRTDirectDirect000DD0No DepositNaNNaN0Transient73.000Canceled2016-04-18
4Resort Hotel0402015August34202320.00BBPRTDirectDirect000DD0No Deposit250.0NaN0Transient176.811Check-Out2015-08-25
5City Hotel02562017July29211220.00BBDEUOnline TATA/TO000AA0No Deposit9.0NaN0Transient-Party107.102Check-Out2017-07-24
6City Hotel1772015July29131220.00BBPRTOnline TATA/TO000AA0No Deposit9.0NaN0Transient76.501Canceled2015-06-29
7City Hotel012016August3240120.00BBBELOnline TATA/TO000AA0No Deposit9.0NaN0Transient151.001Check-Out2016-08-05
8City Hotel01502017April1422221.00BBFRAOnline TATA/TO000AA0No Deposit9.0NaN0Transient135.002Check-Out2017-04-06
9Resort Hotel0902017June26282520.00BBIRLDirectDirect000AA0No DepositNaNNaN0Transient127.000Check-Out2017-07-05
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
119180Resort Hotel1232016June25180120.00HBESPOnline TATA/TO000AA0No Deposit240.0NaN0Transient161.000Canceled2016-06-06
119181City Hotel042016January5280220.00BBPRTDirectDirect000EE0No Deposit14.0NaN0Transient127.000Check-Out2016-01-30
119182City Hotel12862016October43161020.00BBPRTOffline TA/TOTA/TO000AA0Non Refund44.0NaN0Transient90.000Canceled2016-06-20
119183City Hotel01932016September40301220.00BBPRTGroupsCorporate000AA0No DepositNaNNaN0Transient-Party132.000Check-Out2016-10-03
119184City Hotel1202016November4540120.00SCPRTOnline TATA/TO000AD0No Deposit159.0NaN0Transient100.000No-Show2016-11-04
119185Resort Hotel012016June26210110.00BBPRTOffline TA/TOTA/TO000AA0No Deposit104.0NaN0Transient79.000Check-Out2016-06-22
119186Resort Hotel0172016October45301020.00BBPRTGroupsDirect000AA0No DepositNaN346.00Transient-Party66.010Check-Out2016-10-31
119187City Hotel012017April17270120.00SCPRTOnline TATA/TO000AA0No Deposit9.0NaN0Transient160.000Check-Out2017-04-28
119188Resort Hotel0102017June25242130.00BBPRTDirectDirect000AA1No DepositNaNNaN0Transient185.010Check-Out2017-06-27
119189Resort Hotel0562015November48230020.00BBPRTOnline TATA/TO000EA0No Deposit240.0NaN0Transient0.001Check-Out2015-11-23

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date# duplicates
5398City Hotel12772016November4671220.00BBPRTGroupsTA/TO000AA0Non RefundNaNNaN0Transient100.000Canceled2016-04-04180
4175City Hotel1682016February8170220.00BBPRTGroupsTA/TO010AA0Non Refund37.0NaN0Transient75.000Canceled2016-01-06150
5068City Hotel11882016June25150210.00BBPRTOffline TA/TOTA/TO000AA0Non Refund119.0NaN39Transient130.000Canceled2016-01-18108
4872City Hotel11582016May22240210.00BBPRTGroupsTA/TO000AA0Non Refund37.0NaN31Transient130.000Canceled2016-01-18101
3786City Hotel1282017March920320.00BBPRTGroupsTA/TO000AA0Non RefundNaNNaN0Transient95.000Canceled2017-02-0299
3844City Hotel1342015December5080210.00BBPRTOffline TA/TOTA/TO010AA0Non Refund19.0NaN0Transient90.000Canceled2015-11-1799
3900City Hotel1382017January2140110.00BBPRTCorporateCorporate000AA0Non RefundNaN67.00Transient75.000Canceled2016-12-0799
4865City Hotel11562017April17260320.00BBPRTGroupsTA/TO000AA0Non Refund37.0NaN0Transient100.000Canceled2016-11-2199
4199City Hotel1712016June25140310.00BBPRTOffline TA/TOTA/TO000AA0Non Refund236.0NaN0Transient120.000Canceled2016-04-2788
4932City Hotel11662016November4510310.00BBPRTOffline TA/TOTA/TO000AA0Non Refund236.0NaN0Transient110.000Canceled2016-07-1385